Identification of quantitative trait loci associated with canopy temperature in soybean

Abstract

A consistent risk for soybean (Glycine max L.) production is the impact of drought on growth and yield. Canopy temperature (CT) is an indirect measure of transpiration rate and stomatal conductance and may be valuable in distinguishing differences among genotypes in response to drought. The objective of this study was to map quantitative trait loci (QTLs) associated with CT using thermal infrared imaging in a population of recombinant inbred lines developed from a cross between KS4895 and Jackson. Heritability of CT was 35% when estimated across environments. QTL analysis identified 11 loci for CT distributed on eight chromosomes that individually explained between 4.6 and 12.3% of the phenotypic variation. The locus on Gm11 was identified in two individual environments and across environments and explained the highest proportion of phenotypic variation (9.3% to 11.5%) in CT. Several of these CT loci coincided with the genomic regions from previous studies associated with canopy wilting, canopy temperature, water use efficiency, and other morpho-physiological traits related with drought tolerance. Candidate genes with biological function related to transpiration, root development, and signal transduction underlie these putative CT loci. These genomic regions may be important resources in soybean breeding programs to improve tolerance to drought.

Identification and Confirmation of Loci Associated With Canopy Wilting in Soybean Using Genome-Wide Association Mapping

“…Research studies over the past 12 years have identified numerous QTLs from the association and linkage mapping studies that were associated with CW (Charlson et al, 2009;Abdel Haleem et al, 2012;Hwang et al, 2015Hwang et al, , 2016Kaler et al, 2017a;Steketee et al, 2020), canopy temperature (Kaler et al, 2018;Bazzer and Purcell, 2020), and C13 and O18 isotope ratios (Kaler et al, 2017b;Bazzer et al, 2020a,b). This study confirmed 87 SNPs that likely tagged 68 loci as coincident genomic regions from previous studies on CW (Charlson et al, 2009;Abdel Haleem et al, 2012;Hwang et al, 2015Hwang et al, , 2016Kaler et al, 2017a;Steketee et al, 2020), canopy temperature (Kaler et al, 2018;Bazzer and Purcell, 2020), and C13 and O18 isotope ratios (Kaler et al, 2017b;Bazzer et al, 2020a,b).…”

“…To find coincident SNPs or overlap of loci identified in this research study with those loci reported in previous studies (Charlson et al, 2009;Abdel Haleem et al, 2012;Hwang et al, 2016;Kaler et al, 2017aKaler et al, ,b, 2018Bazzer and Purcell, 2020;Bazzer et al, 2020a,b;Steketee et al, 2020), we used Bedtools Intersect Intervals Tool (Quinlan and Hall, 2010) in Galaxy with an overlapping window of ± 175 kb. This window size was chosen because the average LD across all chromosomes decayed at an average of 175 kb in the euchromatic region.…”

“…GWAS identified a total of 188 significant SNPs associated with CW that likely tagged 152 loci. This includes 87 significant SNPs identified as coincident SNPs for CW (Charlson et al, 2009;Abdel Haleem et al, 2012;Hwang et al, 2016;Kaler et al, 2017a;Steketee et al, 2020), canopy temperature (Kaler et al, 2018;Bazzer and Purcell, 2020), C13 ratio (Kaler et al, 2017b;Bazzer et al, 2020a,b), or O18 ratio (Kaler et al, 2017b) from previous studies that likely tagged 68 loci (Table 3), and 101 significant SNPs identified as novel SNPs that likely tagged 84 loci (Table 4). These 152 loci (68 + 84) were identified from the sum of significant loci [-Log10 (P) ≥ 3.5; P ≤ 0.0003] for individual environments, plus loci averaged over siteyears by the IR and DR treatments, and plus loci averaged over all the environments (Figure 2 and Supplementary Figures 1-3).…”

“…The objectives of this study were to confirm the slow-wilting loci identified previously by association mapping (Kaler et al, 2017a;Steketee et al, 2020) and linkage mapping (Charlson et al, 2009;Abdel Haleem et al, 2012;Hwang et al, 2016) and to identify additional novel loci associated with CW using a new panel of 200 diverse soybean accessions. We also considered the association of slow-wilting loci with loci associated with other drought-tolerant traits, such as carbon isotope (C13) ratio (as a measure of water use efficiency) (Kaler et al, 2017b;Bazzer et al, 2020a,b), oxygen isotope (O18) ratio (as a measure of transpiration) (Kaler et al, 2017b), and canopy temperature (Kaler et al, 2018;Bazzer and Purcell, 2020). An additional objective was to determine the ability of genomic estimated breeding values (GEBVs) from previous research studies to identify new slow-wilting genotypes from the United States Department of Agriculture (USDA) germplasm collection.…”

An integrative analysis of yield stability for a GWAS in a small soybean breeding population

“…Quantitative trait loci metanalysis or comparative analysis with reported QTL is normally used to confirm QTL utility for breeding or candidate gene mining (Bazzer & Purcell, 2020 ; Gong et al., 2018; Kaler et al., 2017 ; Qi et al., 2011; Van & McHale, 2017). To identify the QTL region and the linkage with known QTL, it was necessary to develop a new method of LD interval determination, based on probability of molecular markers of belonging to four predefined categories.…”